Various instruments are used to create images of the Earth and other objects in the
universe in a diverse set of wavelength bands with the aim of understanding natural
phenomena. Sometimes these instruments are built in a phased approach, with additional
measurement capabilities added in later phases. In other cases, technology may mature to
the point that the instrument offers new measurement capabilities that were not planned
in the original design of the instrument. In still other cases, high resolution spectral
measurements may be too costly to perform on a large sample and therefore lower
resolution spectral instruments are used to take the majority of measurements. Many
applied science questions that are relevant to the earth science remote sensing community
require analysis of enormous amounts of data that were generated by instruments with
disparate measurement capabilities. We address this problem using Virtual Sensors: a
method that uses models trained on spectrally rich (high spectral resolution) data to "fill
in" unmeasured spectral channels in spectrally poor (low spectral resolution) data.
In a current project, we are using machine learning models trained on the high spectral
resolution Terra MODIS instrument to estimate what the equivalent of the MODIS 1.6
micron channel would have been for the NOAA AVHRR/2 instrument. Since AVHRR/2
has been operating since 1981 but MODIS has only been operating since 1999, our
estimates extend the period of time for which the 1.6 micron channel is available by 18
years. The scientific motivation for the simulation of the 1.6 micron channel is to
improve the ability of the AVHRR/2 sensor to detect clouds over snow and ice.
Virtual Sensors can not only be used to increase the availability of new measurements
further back in time, but it can be used to guide the use of expensive high-resolution
instruments. Models can be constructed to take low-resolution measurements and other
relevant data as input and return estimates of high-resolution measurements with a
measure of confidence. High-resolution measurements can be taken when the model's
confidence is low, thereby limiting the use of expensive high-resolution instruments to
situations when they are needed.